Traders' dilemma: Developing countries' response to trade wars
Bibliographic record
Abstract
Abstract As the United States engages in a trade war with its major trading partners, policymakers in developing countries face the ‘traders’ dilemma’: should they join the trade war, stay out or do something different, including continuing to pursue regional trading arrangements? Using a global, general equilibrium model, we paper simulate an increase in U.S. tariffs to non‐MFN rates and retaliation in kind by its major trading partners—the European Union, China, Mexico, Canada and Japan. We consider four possible responses by developing countries to this trade war: (a) join the trade war; (b) do nothing; (c) form regional trading arrangements with all regions outside the United States; and (d) unilaterally liberalise tariffs on imports from the United States. We find that joining the trade war is the worst option for developing countries (twice as bad as doing nothing); and forming RTAs with non‐U.S. regions and liberalising tariffs on U.S. imports (“turning the other cheek”) is the best. The reason is that a trade war between the United States and its major partners creates opportunities for developing countries to increase their exports to these markets. Liberalising tariffs increases developing countries’ price competitiveness, enabling them to further capitalise on these opportunities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".